ACOUSTIC SPEECH MARKERS FOR TRACKING CHANGES IN HYPOKINETIC DYSARTHRIA ASSOCIATED WITH PARKINSON’S DISEASE

Previous research has identified certain overarching features of hypokinetic dysarthria associated with Parkinson’s Disease and found it manifests differently between individuals. Acoustic analysis has often been used to find correlates of perceptual features for differential diagnosis. However, acoustic parameters that are robust for differential diagnosis may not be sensitive to tracking speech changes. Previous longitudinal studies have had limited sample sizes or variable lengths between data collection. This study focused on using acoustic correlates of perceptual features to identify acoustic markers able to track speech changes in people with Parkinson’s Disease (PwPD) over six months. The thesis presents how this study has addressed limitations of previous studies to make a novel contribution to current knowledge. Speech data was collected from 63 PwPD and 47 control speakers using an online podcast software at two time points, six months apart (T1 and T2). Recordings of a standard reading passage, minimal pairs, sustained phonation, and spontaneous speech were collected. Perceptual severity ratings were given by two speech and language therapists for T1 and T2, and acoustic parameters of voice, articulation and prosody were investigated. Two analyses were conducted: a) to identify which acoustic parameters can track perceptual speech changes over time and b) to identify which acoustic parameters can track changes in speech intelligibility over time. An additional attempt was made to identify if these parameters showed group differences for differential diagnosis between PwPD and control speakers at T1 and T2. Results showed that specific acoustic parameters in voice quality, articulation and prosody could differentiate between PwPD and controls, or detect speech changes between T1 and T2, but not both factors. However, specific acoustic parameters within articulation could detect significant group and speech change differences across T1 and T2. The thesis discusses these results, their implications, and the potential for future studies

Acoustic speech markers for tracking changes in hypokinetic dysarthria associated with Parkinson’s Disease

Joan Ma - ORCID: 0000-0003-2051-8360 https://orcid.org/0000-0003-2051-8360https://icpla2023.at

Multidimensional signals and analytic flexibility : estimating degrees of freedom in human speech analyses

Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis which can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling, but also from decisions regarding the quantification of the measured behavior. In the present study, we gave the same speech production data set to 46 teams of researchers and asked them to answer the same research question, resulting in substantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further find little to no evidence that the observed variability can be explained by analysts' prior beliefs, expertise or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system and calibrate their (un)certainty in their conclusions

Variability in DDK rate productions in typically-developing children in Scotland

Diadochokinesis (DDK) skills are routinely assessed in clinical practice to evaluate potential motor involvement in a child’s speech difficulty. Research on the development of this skill in typically developing children shows a developmental trajectory of DDK rate, with an increased DDK rate and reduced variability in performance as children grow [1, 2, 3] . However, Williams & Stackhouse (2000) found in their cohort of 3-5-year-olds that while consistency of DDK productions improved with age, there was still considerable variability in the rate of DDK productions by age. Further, a review of several studies[4] established high variability in DDK productions within a larger range of age groups (2-17 years). The high level of variability reported for DDK rate warrants further research to better understand the role of variability in charting children’s developmental patterns for DDK performance

Multidimensional signals and analytic flexibility: Estimating degrees of freedom in human speech analyses

Recent empirical studies have highlighted the large degree of analytic flexibility in data analysis which can lead to substantially different conclusions based on the same data set. Thus, researchers have expressed their concerns that these researcher degrees of freedom might facilitate bias and can lead to claims that do not stand the test of time. Even greater flexibility is to be expected in fields in which the primary data lend themselves to a variety of possible operationalizations. The multidimensional, temporally extended nature of speech constitutes an ideal testing ground for assessing the variability in analytic approaches, which derives not only from aspects of statistical modeling, but also from decisions regarding the quantification of the measured behavior. In the present study, we gave the same speech production data set to 46 teams of researchers and asked them to answer the same research question, resulting insubstantial variability in reported effect sizes and their interpretation. Using Bayesian meta-analytic tools, we further find little to no evidence that the observed variability can be explained by analysts’ prior beliefs, expertise or the perceived quality of their analyses. In light of this idiosyncratic variability, we recommend that researchers more transparently share details of their analysis, strengthen the link between theoretical construct and quantitative system and calibrate their (un)certainty in their conclusions

Using a podcast application to collect high quality speech data online for acoustic analysis in people with Parkinson’s Disease

Mridhula Murali - ORCID: 0000-0001-5450-6419 https://orcid.org/0000-0001-5450-6419Parkinson’s disease is a neurodegenerative syndrome that results in various movement abnormalities including a resting tremor, bradykinesia (slow or reduced range of movement), rigidity due to increased muscle tone, a delay in the initiation of movements, and disturbances of postural reflexes. This can lead to secondary conditions such as depression, dementia, swallowing difficulties, and a speech disorder called hypokinetic dysarthria. The aim of this doctoral project is to find robust and ‘trackable’ markers in hypokinetic dysarthria associated with Parkinson’s disease across two time points. These markers could indicate a change in Parkinson’s disease motor symptoms. The original plan for this study was to take speech recordings of people with Parkinson's disease and age matched controls, face to face in a recording studio, for acoustic analysis. However, with the onset of the COVID-19 pandemic, a change of plan was required. The move to online data collection presented several new challenges, while also having a positive effect on participant numbers. This case study focuses on the data collection process used to collect speech data online from people with Parkinson’s disease and a control group for the purpose of acoustic analysis. Issues encountered in collecting reliable speech data online are discussed, including the key factors of consideration such as audio quality, ease of the data collection process for participants, and the available methods to record speech data remotely. Finally, the application of this method of data collection in other linguistics studies and wider use in social sciences is outlined.https://doi.org/10.4135/9781529600575pubpu